High Throughput Image Analysis Library

Foundational image / multimodal models are data hungry; to efficiently feed them with new data it’s imperative to make data QC, collation, processing, analysis, and modeling as modular, performant, and painless as possible. A Python library with templated HPC workflows, a modular CLI, pre-prepared analysis notebooks, and import/export image QC enables rapid, reproducible, and verifiable microscope → knowledge distillation, as well as efficient data curation for training large foundational models.

Motivation

Modern microscopy generates massive datasets:

• Terabytes of images per experiment
• Thousands of experimental conditions
• Multiple imaging modalities and timepoints
• Diverse cell types and perturbations

Processing this data requires:

• Quality Control: Automated detection of imaging artifacts and errors
• Standardization: Consistent preprocessing across experiments
• Scalability: HPC-enabled workflows for large datasets
• Reproducibility: Tracked, versioned, documented pipelines
• Accessibility: Easy-to-use interfaces for diverse users

Solution

A comprehensive Python library providing:

Core Components

1. Modular CLI: Command-line interface for common operations

   • Image ingestion and format conversion
   • Batch preprocessing and normalization
   • Feature extraction and analysis
   • Model training and inference

2. HPC Workflow Templates: Pre-configured for common cluster systems

   • SLURM, PBS, SGE job submission
   • Distributed processing with Dask
   • Containerized workflows (Docker, Singularity)
   • Automatic resource allocation

3. Image QC Pipeline: Automated quality assessment

   • Focus detection
   • Illumination correction validation
   • Artifact detection (debris, bubbles, edge effects)
   • Statistical outlier identification
   • Interactive QC dashboards

4. Analysis Notebooks: Jupyter notebooks for common analyses

   • Exploratory data analysis
   • Phenotype clustering and visualization
   • Statistical comparisons
   • Model evaluation and interpretation

5. Data Curation Tools: Prepare datasets for ML training

   • Balanced sampling strategies
   • Train/val/test splitting
   • Metadata management
   • Export to standard formats (HDF5, Zarr, TFRecord)

Key Features

Performance

• Parallel Processing: Multi-threaded image operations
• Memory Efficiency: Streaming processing for large images
• GPU Acceleration: CUDA-enabled preprocessing when available
• Lazy Evaluation: Process only what’s needed

Reproducibility

• Configuration Management: YAML-based experiment configs
• Provenance Tracking: Automatic logging of all operations
• Version Control Integration: Git-friendly outputs
• Containerization: Reproducible environments

Extensibility

• Plugin Architecture: Easy to add custom operations
• Modular Design: Use only the components you need
• API-First: Programmatic access to all functionality
• Integration-Ready: Works with scikit-image, napari, cellpose, etc.

Architecture

morphexpt/
├── cli/           # Command-line interface
├── io/            # Data import/export
├── preprocess/    # Image preprocessing
├── qc/            # Quality control
├── features/      # Feature extraction
├── workflows/     # HPC templates
├── notebooks/     # Analysis templates
└── utils/         # Shared utilities

Workflow Example

# Ingest raw microscopy data
morphexpt ingest --source /path/to/images --output data/raw

# Run QC pipeline
morphexpt qc --input data/raw --output data/qc_report

# Preprocess images
morphexpt preprocess --input data/raw --output data/processed \
    --operations normalize,background_subtract,register

# Extract features
morphexpt features --input data/processed --output data/features \
    --methods morphology,texture,intensity

# Prepare ML training dataset
morphexpt curate --input data/processed --output data/train \
    --split 0.7/0.15/0.15 --format zarr

Applications

Foundation Model Training

• Large-Scale Curation: Process millions of images for pre-training
• Quality Filtering: Ensure clean training data
• Efficient Storage: Optimized formats for fast loading
• Metadata Tracking: Maintain experimental context

High-Content Screening

• Batch Analysis: Process entire screening campaigns
• Hit Identification: Statistical analysis of phenotypes
• Dose-Response: Automated curve fitting
• Visualization: Generate publication-ready figures

Collaborative Research

• Standardization: Common processing across labs
• Sharing: Package datasets with processing code
• Benchmarking: Compare methods on common data
• Teaching: Notebooks for education and training

Impact

This library enables:

• 10-100x speedup in microscopy data processing
• Reduced errors through automated QC
• Better reproducibility in computational imaging
• Faster iteration on ML models
• Democratized access to advanced image analysis

Technical Stack

• Core: Python 3.9+, NumPy, scikit-image
• Distributed: Dask, Ray
• I/O: Zarr, HDF5, OME-TIFF
• Visualization: Matplotlib, napari
• CLI: Click, Rich
• Testing: Pytest, hypothesis

Future Development

• Integration with more ML frameworks (PyTorch Lightning, JAX)
• Real-time analysis during acquisition
• Cloud-native processing (AWS, GCP)
• Active learning for data curation
• Automated hyperparameter optimization